Method for characterizing images acquired through a video medical device

ABSTRACT

According to a first aspect, the invention relates to a method to support clinical decision by characterizing images acquired in sequence through a video medical device. The method comprises defining at least one image quantitative criterion, storing sequential images in a buffer, for each image (10) in the buffer, automatically determining, using a first algorithm, at least one output based on said image quantitative criterion and attaching said output to a timeline (11).

FIELD OF THE INVENTION

The invention relates generally to image and video processing and inparticular to a system and method to characterize the interpretabilityof images acquired in sequences and especially images acquired through avideo medical device.

BACKGROUND

Video acquisition devices generate massive amounts of data. Efficientuse of this data is of importance for video editing, videosummarization, fast visualization and many other applications related tovideo management and analysis.

As illustrated in Koprinskaa et al., (“Temporal video segmentation: Asurvey.”, Signal Processing: Image Communication, 16 (5), 477-500(2001)), temporal video segmentation is a key step in most existingvideo management tools. Many different types of algorithms have beendeveloped to perform the temporal segmentation.

Early techniques focused on cut-boundary detection or image groupingusing pixel differences, histogram comparisons, edge differences, motionanalysis and the like, while more recent methods such as presented inU.S. Pat. No. 7,783,106B2 and U.S. Pat. No. 8,363,960B2 have also usedimage similarity metrics, classification and clustering to achieve thesame goal.

In some applications as the ones in Sun, Z. et al. (“Removal ofnon-informative frames for wireless capsule endoscopy videosegmentation”, Proc. ICAL pp. 294-299 (2012)) and Oh, J.-H. et al.(“Informative frame classification for endoscopy video”, Medical ImageAnalysis, 11 (2), 110-127 (2007)), the problem of temporal videosegmentation may be reformulated as a classification problem thatdistinguishes between informative and noise images.

In US20070245242A1, temporal video segmentation has been coupled withthe computation of similarity across scenes so as to produce videosummaries.

In the medical device area, and in particular in the field of endoscopy,evaluation of motion patterns has played an important role in theanalysis of long videos.

In U.S. Pat. No. 7,200,253B2, a system to evaluate the motion of aningestible imaging capsule and to display the motion information againsttime is disclosed.

Similar motion information was used in US20100194869A1 for temporalvideo segmentation of endoscopy videos. Fast screening of the content ofthe video is implemented by only displaying the first image of eachtemporal segment; therefore skipping all other images.

To address the same goal of fast video screening in endoscopy butwithout skipping images, US20100194869A1 rely on motion evaluation tocompute a replay speed inversely proportional to the estimated motion.

By relying on video mosaicing tools, an efficient representation ofendomicroscopic videos in which consecutive images have overlap isdisclosed in U.S. Pat. No. 8,218,901B2.

To ease the interpretation of entire endomicroscopic videos, André, B.et al. (“A Smart Atlas for Endomicroscopy using Automated VideoRetrieval”, Medical Image Analysis, 15 (4), 460-476 (2011)) proposed amethod relying on visual similarity between a current video and videosfrom an external database to display visually similar but annotatedcases in relation to the current video.

A similar approach is disclosed in André, B. et al. (“Learning Semanticand Visual Similarity for Endomicroscopy Video Retrieval”, IEEETransactions on Medical Imaging, 31 (6), 1276-1288 (2012)) to complementvisual similarity with semantic information. On a related topic (André,B. et al. “An image retrieval approach to setup difficulty levels intraining systems for endomicroscopy diagnosis”, MICCAI (pp. 480-487).Beijing: LNCS (2010)) presented a means of evaluating a difficulty levelassociated with the interpretation of a given endomicroscopy video.

In clinical scenarios, video analysis may need to be performed duringthe procedure. To work around the issue of computational time(US20110274325A1) discloses a method that takes advantage of a freezedbuffer of consecutive images to perform computationally intensive taskswhile continuing the image acquisition.

As illustrated in the aforementioned work, prior art shows that a realneed exists for efficient use of videos acquired with a medical device.Although efficient use of video data has been addressed both in clinicaland non-clinical scenarios, none of the previous approaches teach amethod to characterize the interpretability of the images composing avideo acquired with a medical device.

Patent document US2006/293558 discloses a method for automatedmeasurement of metrics reflecting the quality of a colonoscopicprocedure. Patent document US2011/301447 discloses a method forclassifying and annotating clinical features in medical video byapplying a probabilistic analysis of intra-frame or inter-framerelationships in both spatially and temporally neighboring portions ofvideo frames.

SUMMARY

One object of the proposed invention is to improve the efficiency of theuse of data acquired with a video medical device. For this purpose, wedisclose a system and method to characterize the interpretability ofimages to support clinical decision. The method disclosed herein isbased on the characterization of images acquired in sequence through avideo medical device and comprises:

-   -   defining at least one image quantitative criterion, also        referred to as the interpretability criterion,    -   storing sequential images in a buffer,    -   for each image in the buffer, automatically determining, using a        first algorithm, at least one output based on said        interpretability criterion,    -   attaching said output to a timeline.

This enables the user of the medical video data to focus its attentionon the most interpretable parts of the acquisition.

Video medical devices to acquire images may be any device known to oneof ordinary skill in the art including, but not limited to:endomicroscopes, optical coherence tomography devices, classicalendoscopy, High Definition endoscopy, Narrow Band Imaging endoscopy,FICE® endoscopy, double-balloon enteroscopy, zoom endoscopy,fluorescence endoscopy, 2D/3D ultrasound imaging, echo-endoscopy or anyother interventional imaging modality.

According to a variant, the method further comprises displaying imagesof said buffer together with said timeline. Advantageously, the methodfurther comprises indicating the position of the displayed image in thetimeline using a cursor of said timeline.

An output of the first algorithm may be a value among a set of discretevalues. The value may typically be an alpha-numerical value. In thiscase the timeline may be formed of temporal regions corresponding toconsecutive images with equal output. These temporal regions mayconstitute a temporal classification or temporal segmentation of thevideo of interest. In the particular case of a binary output, thesetemporal regions may constitute a temporal binary segmentation of thevideo of interest.

An output of the first algorithm may also be a continuous scalar orvector value. In some cases, the algorithm may have two differentoutputs, one being a discrete value, the other one being a continuousscalar or vector value. One example pertaining to diagnosis would be assuch; the first discrete output would indicate a predicted diagnosticclass while the other continuous output would indicate the probabilityof belonging to each pre-defined diagnostic class.

According to a variant, the values of the output of the first algorithmare represented by colors, said colors being superimposed on thedisplayed timeline. The values of the output of the first algorithm mayalso be displayed beside the currently displayed image.

According to a variant, when temporal regions corresponding toconsecutive images with equal output are defined, the method may furthercomprise selecting at least one temporal region and extracting from thebuffer the images corresponding to said temporal regions. The extractedimages may for example be stored on a storage device. The extractedimages may also be processed using a second algorithm and the output ofthe second algorithm displayed. For example, the second algorithm may bea content-based video or image retrieval algorithm, an image or videomosaicing algorithm, an image or video classification algorithm or thelike.

The selection of the at least one temporal region may be performedeither fully automatically or may depend on some user interaction. Forexample, the second algorithm may utilize the complete set of images forall segmented temporal regions. It may also be based on a simpleselection algorithm or may require user interaction to choose theselected regions.

According to a variant, the first algorithm may generate intermediateresults associated with each image of the buffer. The method maytherefore comprise storing said intermediate results into an internaldatabase. The internal database may be for example updated upon eachupdate of the buffer. According to a variant, the first algorithm mayuse intermediate results of the internal database.

When images corresponding to temporal regions are extracted andprocessed using a second algorithm, said second algorithm might use theintermediate results of the internal database.

According to a variant, an interpretability criterion may be kineticstability.

For example, kinematic stability may be evaluated using analysis offeature matches. The features may be located on a regular or perturbedgrid. For example grid perturbation is driven by local image saliency.

A vote map may be used to select and count the number of votes thatdetermines kinematic stability.

Kinematic stability may be initially performed in a pairwise manner onconsecutive images, and a signal processing step may be performed on theinitial kinematic stability signal to provide the kinematic stabilityoutput.

According to the targeted clinical application, the interpretabilitycriterion may be at least one among the non limitative list: kinematicstability, similarity between images, e.g. similarity between imageswithin the buffer, probability of belonging to a category, e.g.probability of belonging to a given category of a predetermined set ofcategories, image quality, difficulty of proposing a diagnosis or asemantic interpretation, image typicity or atypicity or image ambiguity.

Further, an interpretability criterion may use the similarity betweenimages within the buffer and images within an external database.

The above and other objects, features, operational effects and merits ofthe invention will become apparent from the following description andthe accompanying drawings.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a schematic view of a video acquired with a medical devicebeing displayed in association with a timeline highlighting temporalregions of sufficient interpretability.

FIG. 2 is a schematic view of video acquired with a medical device beingdisplayed in association with a timeline highlighting temporal regionslabeled according to discrete values.

FIG. 3 is a schematic view of video acquired with a medical device beingdisplayed in association with a timeline presenting a temporal evolutionof a continuous output.

FIG. 4A is a schematic view of a video acquired with a medical devicebeing displayed in association with a timeline highlighting temporalregions of sufficient interpretability and FIG. 4B illustrates a set ofcases comprising video and additional metadata that have been selectedfrom an external database according to a similarity criterion withrespect to the current temporal region.

FIG. 5 is a diagram illustrating matching of consecutive images andthresholding based on matching quality.

FIG. 6A and FIG. 6B are diagrams illustrating a refinement strategy forpositioning local image descriptors.

FIG. 7A and FIG. 7B are diagrams illustrating the processing of aninitial interpretability label timeline for outliers removal.

DETAILED DESCRIPTION

In a basic mode of operation, a medical video acquisition device acts asan input to our system. Real-time video processing may be performedduring acquisition, and the images may be displayed. In the meantime,the images are queued in a finite first-in-first-out (FIFO) buffer whilethe potential results of the real-time computation may be stored in aninternal database.

In a second mode of operation, our system may use a video that waspreviously recorded by a video medical device as input. In this case,the images composing the video are also queued in a FIFO buffer. Ifreal-time computation was performed during the acquisition and wasrecorded together with the images, the results from the computations maybe loaded in an internal database.

In both modes of operation, the internal database might be updated eachtime the image buffer gets updated.

Upon review of the images stored in the input buffer, our systemautomatically characterizes the interpretability of the images composingthe buffer and attaches its output to a timeline corresponding to thecontent of the images in the buffer. The characterization of theinterpretability may rely on previously performed real-time computationsas well as post-processing computations.

Depending on the targeted clinical application, interpretability may becharacterized according to different underlying criteria. These criteriamay be related to different notions such as, but not limited to:

-   -   kinematic stability,    -   similarity of the images within the buffer,    -   amount of new information uncovered by an image with respect to        previous ones,    -   image quality,    -   presence and importance of artifacts,    -   nature and type of imaging artifacts,    -   probability of belonging to a given category, e.g. diagnostic        class, within a predefined set of categories,    -   image typicity or atypicity,    -   image ambiguity, e.g. visual ambiguity with respect to a set of        diagnostic classes,    -   difficulty of proposing a diagnosis or a semantic        interpretation.

In endomicroscopy, an imaging probe is typically put in contact with, orput close to, the tissue to acquire images. Real-time acquisition may beperformed thanks to mirror scanning across the field of view. Because ofthe continuous motion of the probe with respect to the tissue during themirror scanning, the images are subject to motion artifacts. Themagnitude of these artifacts is typically correlated to theinterpretability of images. Indeed, if too much motion is observed, thecellular architecture of the tissue may be strongly distorted and maylead to images that are difficult to interpret.

In most video medical devices, a user will navigate an imaging probe oran imaging detector on or within the patient and will stay onto an areafor a time that is correlated to the interest and interpretability ofthe area.

As such, in some embodiments of the present invention, interpretabilitymay be a function of the motion of the imaging probe with respect to itsobject. In other words, in some embodiments, interpretability may becharacterized in terms of kinematic stability.

In other scenarios, relating interpretability to model-basedcomputational features might be complex to perform. It might however bethe case that an external database of images has been previouslyacquired and annotated according to some interpretability criteria byexpert users. In other embodiments of the invention, machine-learningalgorithms may be used to infer the interpretability of new images bylearning from the available annotated external database.

In still other scenarios, interpretation of a video might rely onidentifying the variability of the images acquired with the videomedical device. In this case, the user might be interested in havingsimilar images being grouped together. Embodiments of the invention mayuse other forms of machine learning to characterize the interpretabilityby clustering images according to their similarity.

Several visualization techniques can be used to display at least oneimage characterization output, while the user is playing an alreadyrecorded video, playing a buffered video, or visualizing the imagecurrently being acquired by the video medical device. For each imagestored in the buffer, the computed output value may be discreteinformation, such as a letter or label, or a continuous scalar or vectorvalue.

As illustrated in FIGS. 1 to 4, the output values may be attached to atimeline 11 of the video, where the timeline 11 comprises a temporalcursor 15 that indicates the time of a displayed image 10 in thetimeline 11. According to one embodiment of the invention, colorsrepresenting the output values computed for all the images of the videoare directly superimposed in the timeline of the video, in order toprovide the user with a chronological view of image interpretabilitywithin the video. A legend explaining the output values may also bedisplayed to ease user understanding.

The output value computed for the currently displayed image 10, or acolor representing this value, may also be displayed beside thecurrently displayed image, in order to duplicate the output valuepotentially hidden by the current temporal cursor 15, as illustrated inFIG. 2 (element 29).

In case of a discretized output, each output value may be represented byone predefined color. FIG. 1 illustrates the case of a binary outputrepresented by a predefined gray 12 or white 14 color in the timeline11. Gray (respectively white) color at a given position in the timelinemay indicate for example that the image at this position in the video isof sufficient (respectively insufficient) quality, or that it iskinematically stable (respectively unstable) with respect to theprevious image.

FIG. 2 illustrates the case of an output discretized into four distinctvalues, each of them being represented by a distinct color: white 24,light gray (dot) 28, dark gray (wave) 22 or black (hatched) 26. If thereis an order relation between the output values, this order can be keptbetween gray levels to which the values are mapped. If not a randomorder may be chosen. These four gray values may indicate for examplefour interpretability levels ordered as: not interpretable at all,hardly interpretable, partially interpretable, fully interpretable. Theymay also indicate for example: not sufficiently interpretable,sufficiently interpretable and belonging to tissue type A, sufficientlyinterpretable and belonging to tissue type B, sufficiently interpretableand belonging to tissue type C, where there is no order relation betweenthese three tissue types.

In case of a continuous output (FIG. 3), each output value may still berepresented by a color that can be automatically determined by mappingthe output value for example to a RGB, HSL, HSV or YUV triplet value. Alookup table may be used to convert continuous outputs into colors. Ifthe output is a n-dimensional vector with n≤3, the same mapping processcan be adapted. If the output is a n-dimensional vector with n>3, themapping process can be computed for example from a 3-DimensionalPrincipal Component Analysis. The continuous color value 32 may indicatefor example the image quality, or the percentage of local regions in theimage that match with a local regions in the previous image. FIG. 3illustrates how such visualization may allow the user to appreciate thetemporal evolution of a continuous image interpretability value withinthe video.

In the particular case where the user is only visualizing the imagecurrently being acquired, at least one output value may be computed onthe fly for this image. Said output value, or a color representing thisvalue, may be displayed beside this currently acquired image.

In many cases, the user of the video data is not only the physiciandirectly but may be a second computational algorithm. We disclose anembodiment of the invention in which the characterized interpretabilityis used to perform further computations solely on temporal regions ofadequate interpretability.

In case of a discrete output attached to the timeline, temporal regionscan be defined in the timeline as the largest segments corresponding toconsecutive images with equal output value. The current temporal regionis defined as the temporal region to which the current temporal cursorbelongs. User interactions may then be defined, allowing the user topotentially:

-   -   Disable or enable the display of at least one output;    -   Move the temporal cursor to the closest next time point which        belongs to a temporal region distinct from the current temporal        region;    -   Move the temporal cursor to the closest previous time point time        point which belongs to a temporal region distinct from the        current temporal region;    -   Select at least one temporal region;    -   Refine and modify the temporal regions    -   Store the images associated with the selected temporal region        onto a storage device, and potentially annotate them;    -   Launch at least one second algorithm on the current temporal        region, or on at least one temporal region selected by the user.        Said second algorithm uses as input the image subsequence(s)        associated with the temporal region(s). A second algorithm may        for example consist in classifying or mosaicing these input        image subsequence(s).    -   Visualize at least one output created by at least one second        algorithm, said second algorithm being potentially automatically        launched on the current temporal region. Advantageously, this        second output may be automatically displayed, without requiring        any user interaction.

In this scenario with a second algorithm, the interpretability can alsobe defined in terms of how the data is used by the subsequentcomputations. Dedicated video mosaicing techniques can be used to widenthe field of view of a video by aligning and fusing many consecutiveimages from a video sequence. This process only works if consecutiveimages share a sufficient overlap and if some motion between the imagingdevice and the object of interest is observed. In one embodiment of theinvention, interpretability may be defined in terms of kinematicstability and video mosaicing tools may be applied on the regions ofsufficient interpretability.

According to another embodiment, if video mosaicing has been applied onat least two video subsequences to produce larger field of view images,image mosaicing technique may subsequently be used to detect andassociate matching image mosaics, spatially register them and fuse themso as to create even larger field of view images. The detection ofmatching mosaics may also depend on user interaction.

To ease the interpretation of video sequences acquired with a videomedical device, content based video retrieval tools can be used as ameans of leveraging similarity-based reasoning. For a given videosequence, the physician may be presented, from an external database, aset of cases visually similar to the video sequence and previouslyannotated by experts. Video sequences acquired with a medical device maycontain parts of variable interpretability, and may contain a mix ofdifferent tissue types. As such, the relevance of these content-basedvideo retrieval tools may critically depend on choosing, as request, aportion of a video which is consistent in terms of interpretability. Inone embodiment of the invention, interpretability characterization isused to automatically split an input video into sub-portions ofsufficient interpretability; said sub-portions being used to constructat least one query for a content-based video retrieval algorithm.

According to one variant, the sub-portions may be used in differentmanners to create the query for the content-based retrieval algorithm.For example, each sub-portion may be used to create an independentquery. Alternatively, the entire set of sub-portions may be used tocreate a single query. Still alternatively, the user may be required toselect a subset of these sub-portions to create a single query.

According to another variant, the user also has the ability to refinethe temporal segmentation provided by the first algorithm beforeresuming to the second algorithm.

FIG. 4A and FIG. 4B illustrate the case where the second algorithm is acontent-based video retrieval processing that has been launched on thecurrent temporal region of the video of interest. The output created bythis second algorithm and displayed to the user consists of threereference videos (41, 42, 43) together with their annotations (44, 45,46), where the annotations include for example the diagnostic class ofthe reference video. These reference videos have been extracted from anexternal database as the most visually similar to the set of contiguousimages associated with the current temporal region selected by thecursor 15 in FIG. 4A.

According to another embodiment, in the case of discrete labels, theinvention also allows to automatically run a second algorithm on each ofthe regions.

According to another embodiment, in the case of discrete labels, theinvention also allows to automatically store the content of all labeledregions independently, or in the sub-case of binary labels, to store ona storage device the concatenation of all temporal regions correspondingto a given label.

Kinematic Stability

Image registration-based approaches can be used to identifykinematically stable temporal regions within video sequences. This canfor example be done by actually registering temporally consecutiveimages and then analyzing the quality of the spatial transformationfound by the registration algorithm.

Another example would be to use only a subset of the steps of an imageregistration algorithm and analyze the quality of the results providedby this subset. This can be done in the case of feature matching-basedalgorithms where looking at the consistency of the feature matches witha spatial transformation model could allow one to infer informationabout kinematic stability.

The same feature matches may also be analyzed in terms of localconsistency so as to obtain a result that is more robust to modelingerror for the spatial transformation.

More advanced methods registering multiple images at the same time, suchas the one presented in (Vercauteren, Perchant, Lacombe, & Savoire,2011) may also be used to infer kinematic stability.

FIG. 5 illustrates in more detail one possible embodiment for analyzingkinematic stability relying on a grid of features. Each image 52 of aseries of sequential images 51 stored in the buffer in the buffer isassociated with a grid (57) of spatial locations on the image (step I).Each point (58) of the grid (57) is associated with a local spatialregion with a given scale around that point, each region in turn beingassociated with a descriptor, or numerical signature. Matching eachdescriptor from one image to a numerically similar descriptor from theprevious image (step III), allows one to match each point of a grid (59)in an image (54) to another point on a grid (57) of the previous image(53); said matched points are associated with local regions that arevisually similar thanks to the descriptor being similar. Analysis of thematches is then performed to evaluate their local consistency or theirconsistency with respect to a predefined spatial transformation model.If the consistency is estimated to be too low, the image will beconsidered as kinematically unstable with respect to the previous one.

Representing an image as a grid of descriptors is often referred to asdense local image description or dense description in short.Interchangeably, we may also use the term grid-based for theseapproaches. Each point of the grid may also be referred to as akeypoint.

One advantage of relying on grid-based local image description, is thatthe same descriptors may be used both to characterize the stability ofvideo sequences and to perform content-based video retrieval task. Thiswould allow to save computational time in the case where both tasks areto be performed.

Local image description, grid-based or not, is widely used in computervision, pattern recognition and medical imaging and has served a varietyof purposes. Many different descriptors are now available including butnot limited to LBP, SIFT, SURFT, HoG, GLOH and the like. Depending onthe exact application, different computational requirements, performancerequirements, ease of implementation requirements, etc., may lead toeach option.

Keypoint localization is sometimes crucial in computer vision. In mostcases, a regular grid of keypoints is not the most common choice. Insome scenarios, it is advantageous to have keypoints being preciselylocated on the most salient points.

Typically, first and second derivatives of the image may be used todetect the most salient points as well as to estimate the scale of thecorresponding local region. The well-known Harris detector for exampleuses the trace of the Hessian matrix to detect corners. Other detectorsuses a Laplacian estimator which is the determinant of the Hessianmatrix. Once the most salient points are detected, keypoints can be seton the corresponding locations with a scale provided by the saliencydetector.

As in the grid case, keypoints derived from salient points can then beused to compute local image descriptors. A discrepancy measurement maythen be computed between descriptors, resulting in keypoint matches,which may be analyzed or regularized by a transformation model. Exampletransformation models include, but are not limited to, projective modelswell suited for camera applications, translation models and rigid-bodytransformation models both being well suited for microscopy applicationsand deformable models that can encompass tissue deformation.

Keypoint matching methods typically have several constraints. Forexample, it is often the case that good matching performance mandateskeypoints to be localized on sufficiently salient points but also to bewell distributed over the image field.

Having the keypoints located on sufficiently salient points willtypically make the localization of the keypoints more robust withrespect to change of the imaging parameters. This may therefore improvethe performance of the registration algorithm by making the keypointmatching more accurate.

During the keypoint matching process, it is often better to have asingle response while trying to associate a keypoint with many others.It is also often desirable to avoid having spatial regions in the imagewithout keypoints. This calls for a good distribution of the keypoints.

It is also often advantageous to choose descriptors that are invariantunder different acquisition effects including but not limited to:

-   -   Intensity changes. The observed image signal may indeed change        depending on global and local light reflection, on power of the        illumination, on photobleaching effect, on imaging artifacts and        so on.    -   Spatial distortions. The observed morphology of the described        area may change depending on the point of view; the tissue may        change between different images because of respiration,        heartbeat, contact with instruments; the user may change the        zoom of the instrument; the device may produce artifacts and so        on.

In some scenarios, the description and discrepancy measurement processmay benefit from mimicking human vision as close as possible. It is atleast most often advantageous to choose a description-discrepancy couplesufficiently relevant to correctly associate region from one image toanother most of the time.

Although salient point detection followed by standard local regiondescription answers most of the constraints in several applications, ithas been shown to fail finding well-distributed salient regions on manydifferent medical imaging problems. Medical images are indeed oftensmooth but textured and lack the edges of corners that many computervision specific tools require.

To answer these constraints in the context of medical imaging, applyinga grid-based description at fixed scales on medical images is often aninteresting choice. Information may indeed be distributed everywhere inmany medical images.

Relying on grid-based description for registration purpose is oftenthought as a challenging task. Compared to saliency-detection-basedmethods, the choice of the description-discrepancy couple has moreimpact of the matching accuracy. It also generated a significantlylarger number of outlier matches that needs to be handled by the method.

Some imaging scanning devices that are used in the clinical field mayalso lead to rather strong motion artifacts. If the tissue is in contactwith an imaging probe, this may result in complex to predict orunpredictable deformations.

In the following, we focus on one example descriptor, the SIFTdescriptor that has been shown to be efficient on some medical imagingproblems, to illustrate some of the concepts of local image descriptors.It should be recalled that any other local image descriptor may be used.

The SIFT (Scale Invariant Feature Transform) algorithm includes bothkeypoint detection and image description. With the grid-baseddescription approach, keypoint detection may not be required and onlythe descriptor part of SIFT may be used.

Gradient information can be used to describe a local region of an image.More specifically, histograms of oriented gradients have shown efficientresults. Within a local image region, a histogram may be created indifferent sub-regions of the local region to sum up the magnitude of thegradients in the sub-region according to some discretized orientationbins. The entire local image region may then be described by one finalhistogram that concatenates all sub-region histograms in a pre-definedorder.

The notion of windowing also often plays an important role to betterweight the contribution of gradient magnitude over the descriptor.Windowing is typically applied on the entire descriptor. Gaussiankernels are the most common windowing choice but any other type ofwindow (Blackman, Hamming, Cosine . . . ) may be used.

Gaussian windows have an infinite support, a practical implementation ofit may rely on truncation or more complex forms of approximations suchas recursive filtering. In many cases, it can be advantageous totruncate the support of the Gaussian window after a distance thatdepends on the standard deviation σ of the Gaussian window. Typically,the truncation distance r can be chosen to be proportional to σ. It isfor example classical to use r=σ/2 but any other relationship could beused.

Once a windowing strategy has been defined, the windowing values can beused in the creation of the descriptor by weighting each gradientinformation according to the windowing function during the finalhistogram creation.

In some cases, it might be advantageous to obtain local descriptors thatare invariant under any rotation of the image. This may be achieved bymany different means including, but not limited to:

-   -   finding a mode or mean of the orientation within the entire        local region and reorienting the region or the gradient values        according to this principal orientation    -   using circular-shaped bands to subdivide the local region in        sub-regions

Defining a principal orientation for the descriptors region may forexample be done by computing a first gradient orientation histogram onthe entire local region of the descriptor. This histogram creationprocess may be different than the sub-region histogram creation one, forexample:

-   -   the number of angular bins used to compute the principal        orientation may advantageously be larger than the number of        angular bins used to compute the sub-region histogram. This may        permit to have a more accurate re-orientation strategy        potentially leading to a higher invariance with respect to        rotation changes.    -   a different windowing function might be used to weight the        contribution of each gradient sample.

If principal orientation is defined as a mode of the orientationhistogram of the entire local region, the highest peak in this gradienthistogram will provide the value of this principal orientation.Similarly a mean value may be wanted, in which case using a Fréchet meanon the orientation histogram might be advantageous to take into accountthe wrapping of angles at 360°. Finding the peak may also benefit fromusing a certain form of regularization by fitting a local model such asa spline or a Gaussian to identify the location of the peak with sub-binaccuracy and in a potentially more robust manner.

If a mode is used for the definition, we may also want to use severaldifferent modes to create several descriptors, one per selected mode.Selecting several modes can for example be done on the basis of acomparison between the highest peak and the secondary peaks. If theheight of the secondary peak is sufficiently close to the highest one,for example above some fraction of it, it might be interesting to keepit. Determining the corresponding threshold might be done throughdifferent means, including but not limited to rule of thumb, trial anderror, cross-validation, optimization and the like.

Once the principal orientation is given, sample gradient orientationvalues can be distributed in the gradient histograms of the sub-regionsusing angular difference and tri-linear interpolation. As such, positionand angle of samples may be taken into account during the interpolation.

One advantage of using a circular truncation and a circularly symmetricwindowing function is that it may save some computational time byallowing avoiding checking whether a sample is inside or outside thetruncation region after the re-orientation.

It should be noted that re-orientation is not always a necessity. Forexample, if it can be assumed that if no, or very little, noticeablerotation between consecutive images of the video can be observed,rotation invariance may be useless or even detrimental as it may lead tohigher computational requirements. Absence of noticeable rotation inconsecutive images is for example the standard case in endomicroscopicvideos. Indeed, the imaging probes typically have a high resistance totorque. Rotation of the imaging probe with respect to the tissue cantherefore often be neglected.

One important notion in local descriptors is the determination of atleast one scale of observation. This scale may be automatically definedof may be fixed thanks to application-specific knowledge. In the contextof keypoint detection, scale is typically determined during thedetection process. In the context of grid-based approaches, fixing apredefined-scale might appear as a more natural choice. However, otherchoices might be made.

As mentioned above, choosing a predefined scale can be done according toapplication-specific knowledge. For example, when using endomicroscopy,it might be advantageous to use a scale or scales that is or are relatedto anatomically meaningful scales, such as a few microns to focus on afew cells only, a few tens of microns to focus of cellular architecturepatterns and so on.

According to another embodiment of the invention, at least one optimalscale may also be detected either of a training database of on theentire set of images by optimizing some form of intra-image energy atthe given scale or by optimizing the average saliency across the entireimage at the given scale.

Once a scale is given, it might be advantageous to resample the localimage region to an image patch with a given fixed pixel size. This maybe done with standard scale-space approaches. A typical scale spacetransformation of an image I(x,y) can be defined byL(x,y,s)=G(x,y,s)∘I(x,y) where s is the scale factor and ∘ is theconvolution operation in x and y, and G is a 2D Gaussian function. Thisscale-space is used to smooth the local regions before down samplingthem to the desired fixed size.

It might be advantageous to consider that input images are alreadynaturally smoothed by a certain σ₀ arising from some parameters such asthe quality of the optics, the image reconstruction process, etc. Thevalue of the standard deviation used for smoothing the images beforedownscaling may account for this natural smoothing, for example by using√(s−σ₀) instead of s directly.

When a grid-based approach is taken and a fixed scale of observation isprovided, it might be advantageous to choose a grid step that issufficiently small to capture all possible structures which actuallyexist in the image but sufficiently large to reduce computationalrequirements.

One advantageous choice can be to choose a grid step to be proportionalto the scale factor. To reduce the computational cost, it might also beadvantageous to choose an integer proportionality factor. This way,resampled pixels and samples for the local descriptor will beco-localized. One step of sample interpolation may thus be avoided.

Although a grid approach often shows accurate and efficient results, insome scenarios, it might be advantageous to refine the matching resultsfrom the grid. Indeed, the accuracy of a match is limited to the gridstep. Reducing the grid step is an option but this is at the price ofincreasing the computational cost. In one embodiment of the invention, aform of dithering can be used on the grid point positions to randomizethe quantization error and thus lower its average.

As illustrated in FIG. 6, intentional noise can be added to the regulargrid (62) point positions 63 to create a disturbed grid 64. Preferably,the standard deviation of this noise would be less than a fourth of theoriginal grid 62 step to keep the point positions 65 of the disturbedgrid 64 sufficiently close to the original one. This is potentiallyimportant to ensure a sufficient coverage of the entire image.

In another embodiment, original points would be seen at seed points,which could each generate several points with different instances ofnoise. Choosing one noisy instance per seed point would lead to a simpledisturbed grid but choosing higher number of instances might bebeneficial.

In still another embodiment, the noise added to the grid point locationswould not be made at random but would be driven by the saliency mapcorresponding to the underlying image. Starting from an original regulargrid of points, each grid point would be attracted by nearby salientimage points as well as being attracted by the original location. Thecompetition between the two attractions would define the final positionof the disturbed grid point. Similarly, we could also add a repulsionterm between the grid points. With this approach, the descriptors wouldbe well distributed over the image but would also focus on salientpoints within the image, potentially making the matching more accurate.

In more detail, according to one example setup, the attraction to theoriginal grid point could be binary with no attraction as long as thepoint is within a bounded circular region and infinite attraction whenthe point is outside of the bounded region. If no grid points repulsionterm is used, the grid point would then end up being co-localized withthe most salient image point within the bounded region.

The derivation of the image saliency map can be done using standardsaliency criterion, such as but not limited to second-orderderivative-based criteria or information theoretic criteria.

As illustrated in FIG. 5, once an image description 54 is available, thedescriptors 59 of this image can be matched to the descriptors 57 of theprevious image 53 in the buffer. The set of matches (II) can now beanalyzed to evaluate whether the motion was stable or not between thesetwo images.

To find good descriptor matches, one possible choice is to rely on the kclosest descriptors as provided by a discrepancy measurement. Severalalgorithmic approaches to leverage closest points are disclosed.

To measure the discrepancy between two descriptors, Euclidean distancewould be the simplest choice, often producing sufficient results. Otherdiscrepancy measurements relying on distances, pseudo-distances or moread-hoc algorithms may however be used, including but not limited to χ²,Mahalanobis distance, Earth Mover's Distance (EMD), and the like. Insome scenarios, using such discrepancy measurement could potentiallylead to better results for feature matching purposes.

Euclidean distance is widely used to compare any points of anydimension. However, descriptors may be normalized and could for examplerepresent the local distribution of gradients within a region ofinterest. In this scenario, the discrepancy measurement between thedescriptors could benefit from relying on probability density relateddistances such as the EMD.

Even in the above case, Euclidean distance or squared-Euclidean distancemay be of high interest for computational reasons.

Given a discrepancy measure, we may compute every possible pairwisediscrepancy between two sets of descriptors. This allows for thecreation of a discrepancy matrix D, where D(i,j)=discrepancy(i^(th)descriptor from 1^(st) set, j^(th) descriptor from 2^(nd) set). Thisposes two potential problems. The first one is that of computationalcomplexity to create the D matrix. The second one is that this processmay generate a large number of outliers. Improving both aspects would beuseful. To reduce the computational cost, we may for example toleratesome error on the matching by relying on approximate nearest neighbortools rather than exact nearest neighbor. To reduce the number ofoutliers, it is for example possible to validate each match beforeadding it to the list of useful matches. Such step may require not onlyto focus on the closest match but also to look for the k closestmatches.

Looking at computational complexity of the brute force approach, if weconsider looking for the k best matches over two sets of N descriptors,each descriptor having the same size n, the complexity of the bruteforce k-nearest neighbor (k-NN) search algorithm is exactlyO((C(n)+k)·N²), C(n) being the cost of the discrepancy measurement. Inthe case of Euclidean distance, C(n) is roughly equal to n. The cost topartially sort each row in order to get the k better results is O(kN) onaverage. The complexity of the exact search is thus O((n+k)·N²).

To reduce the computational complexity, approximate nearest neighbortechniques may be used. This reduction may for example be achieved byrelying on data partitioning. A binary n-d tree is built to separatepoints of dimension n. This is recursively done for each child until thecardinal of point of a leaf reaches one. Building this tree while usinga median-split as clustering has a linear complexity of θ(nN log₂(N)).It should be noted, that any clustering method could be used to splitdata into the binary tree. Commonly, a simple median-split is typicallyused but hierarchical K-means or other clustering algorithms are alsowidely used for this specific application.

Once the n-d tree is built, the search algorithm goes from the top oftree to a final leaf to reach the first closest point. The complexity toapproximately search the k closest points of N queries is about O(kNlog(N)). The complexity of n-d tree construction and approximate searchin the n-d tree is: O((n+k)N log(N)).

In the basic mode of operation, we could for each pair of images tomatch, build the n-d tree for the first (respectively second) image andmatch each descriptor from the second (resp. first) to its k closestdescriptors in this n-d tree. Both orders may also be performedconcurrently if required.

To further save computational time, it can be advantageous to build onen-d tree only every two images. This can be achieved if we can choosewhich of the two images is used to create the n-d tree. Indeed, we canstart by choosing the second image for the creation of the n-d tree,then, when a third image is to be matched to the second one, the n-dtree for the second image would be used as it is already available. Whenthe fourth image is to be matched with the third one, a new n-d treewould be built for the fourth image and so on.

For the purpose of transferring a n-d tree from one image pair to thenext, the invention advantageously may make use of the internal databaseintroduced earlier.

Given the brute force approach or more advanced ones, each descriptor inthe first set can be associated with the closest descriptor in thesecond set. This matching is not necessary symmetrical. Symmetrychecking feature can advantageously be used to validate a match and thusto remove outliers. Given the best match, in the second set, of adescriptor from the first set, if the closest descriptor, in the firstset, to the descriptor of the second set is exactly the same descriptoras the initial one from the first set, then, the match would bevalidated. An implementation of symmetry checking may benefit frombuilding and storing one n-d tree per image.

Although symmetry checking may allow removing many outliers, it may bebeneficial in some cases to further refine the outlier removal.Eliminating most of the wrong associations would permit producingeasier, more accurate and more robust analysis of the matches. Typicalcases leading to wrong matches include but are not limited to:

-   -   Out of overlap descriptors. For any non-trivial spatial        transformation relating two consecutive images, although there        might be an overlap between the consecutive images, there will        in most case be spatial regions in the first image that do not        exist in the second image. For those descriptors in the        non-overlapping regions, there exist no good descriptors in the        other image to be associated with.    -   Flat descriptors. Regions with very little contrast or flat        regions in the image do not have any reliable gradient        information. Distribution of gradient is homogeneous, driven by        the inherent noise of the imaging system. This may lead to        random matches between the flat regions. The same problem may        appear in a less stringent way for regions that only show        contrast along a single direction. This is the so called        aperture problem.

It should be noted that the symmetrization disclosed above may help inremoving many outliers in these two categories. There are however casesfor which other methods may be more beneficial. Some imaging devices mayindeed create a static noise pattern on top of their images due tocalibration inaccuracies, vignetting effect, scratches on the optics andso on. In this set setup, images with no useful contrast still have asmall contrast arising from any of the aforementioned artifacts. Flatregions may therefore not be completely flat. Weak gradient informationfrom that static noise may then be taken into account while associatingdescriptors. These bad matches will potentially not be removed bysymmetrization and will bias the matching towards the identity.

To determine if a match is reliable, ratio analysis between thediscrepancy of the current descriptor with its closest descriptor in theother set and the discrepancy with its second closest descriptor hasbeen proposed. While this works well in practice when keypoint detectionis used, this fails to work properly in the grid case where overlappingregions may be described and may thus have similar descriptors. Keypointdetection may lead to descriptor positions ensuring that all localregions describe almost non-overlapping regions within the input image.When using a grid-based image description approach, regions covered bydescriptors may have a non-negligible overlap. There are for examplecases where around 80% of overlap appears to be beneficial. It wouldthen mean that the descriptors of two spatially neighbor local regionscould be similar. Therefore the closest descriptor and the secondclosest one could in turn have very similar discrepancy with the currentdescriptor.

According to one embodiment of the invention, ratio analysis between thediscrepancy of the current descriptor with the closest descriptor in theother set and the discrepancy with the k^(th) closest descriptor can beused. The choice of k has to be made keeping in mind the structure ofthe grid. For example choosing k=5 (resp. k=9) ensures that the direct4-connected (resp. 8-connected) grid points to the best match are nottaken into account. A threshold on this ratio may allow removing manyoutliers while keeping most of the inliers in.

Such ratio analysis should provide usable results because comparing acorrect match with the closest incorrect one should lead to much higherdifference than comparing an incorrect match and the closest otherincorrect match. Standard approached have used the first closest matchas a comparison point while we disclose using the k^(th) one to avoidtaking into account almost all correct matches from regions having highoverlap with the correct match. As mentioned above, it is beneficial toadapt the parameter k depending on the density of the descriptor gridused. The denser the grid is, the further we need to look for the seconddescriptor used in the ratio.

According to another embodiment of the invention, it is also possible toremove all the matches with a discrepancy above a given threshold. Thethreshold can be a globally predefined one, can be computed globally fora given pair of images based on the observed statistics of thediscrepancies, or can be computed locally based on the discrepanciesobserved in a local neighborhood of a point. More complex options takinginto account the actual content of the local image region descriptorsmay also be imagined.

Given a pair of consecutive images and set of filtered matches, we maynow proceed with their analysis to evaluate the kinematic stability fromone image to the other.

According to one embodiment, the analysis of the matches would beperformed as such: the matches would vote within a set of discretizedspatial transformation parameters, thus creating a vote map. Theparameters that have a sufficient number of votes would be considered asconsistent votes. The percentage of consistent versus inconsistent votescould then be used as a confidence evaluation for the kinematicstability.

Given a pair of consecutive images and set of filtered matches, we mayalso want to estimate a spatial transformation that allows registering,or aligning, the images. For medical images, such registration is oftena potentially challenging task due to, but not limited to, some of thefollowing reasons.

When imaging the same tissue region at different time points, theobserved image signal may vary due to specular reflection,photobleaching, changes in vascularization or oxygenation, changes inthe amount of excitation light and so on.

Occlusion might occur due to the presence of other instruments, of bloodand other biological liquids, smoke, feces, etc.

The tissue structures can also be deformed due to the respiration,heartbeat, patient motion or contact between tissue and instruments,including the imaging probe. Local deformations may thus need to betaken into account while registering two consecutive images.

The imaging device may also generate its own motion artifacts that mayin some cases be too complex to be properly modeled for the task apairwise image registration. For example, in the case of an imagingscanning device, scanning of the imaging field of view for a given imagemay be performed thanks to mirrors. This implies that each pixel may beacquired at a different time. When the imaging probe is moving withrespect to the tissue, it may cause strong distortions that are varyingwithin the field of view. In some cases, if the motion of the imagingprobe with respect to the tissue is constant while acquiring a image,the distortions can be modeled and compensated for. However, in mostcases the motion of the probe is more complex and cannot be easilymodeled especially if the motion evolves rapidly.

In some scenarios, the imaging device relies on image reconstruction andcalibration information to produce its images. The calibration may haveinaccuracies and may even change over time. This may lead to either astatic noise pattern that may bias the image registration or to a changein the visual appearance that may complexify the task of imageregistration.

In most cases, the imaging device has no tracking information that wouldbe helpful to guide the image registration process. Also, even whentracking information is available, the accuracy of it might be quitelarge in comparison to the field of view. This would be especially truein the field of Endomicroscopy but would also hold for most imagingdevice because of patient motion.

In some cases, even though the above reasons still exist, their impacton the images could be sufficiently small that we can directly estimatea spatial transformation between the images and analyze the result todecide on the kinematic stability. In other cases where the same reasonshave a higher impact on the images, such an approach may only work for asmall percentage of image pairs. This may therefore lead to a biastowards instability in the estimation of kinematic stability. Indeedmany pairs of images could potentially not be properly registeredalthough the overall motion between the images could be considered assmooth.

According to one embodiment of the invention, we focus on cases forwhich finding a spatial transformation model is sufficient to estimatekinematic analysis. The spatial transformation could be any of theclassical or less classical models including, but not limited to,translations, rigid-body transformations, affine transformations,projective transformations, translations with shearing to account formotion distortions and so on. In this scenario, the matches may serve asinput data to fit the transformation model. Optimization-based schemessuch as gradient descent, simulated annealing and the like or randomsampling schemes such as RANSAC, MSAC and the like, least-squaresfitting, least-trimmed squares, weighted least-squares fitting, L₁fitting and the like may all be used. Hierarchical fitting approaches,such as those that progressively refine the spatial transformationmodel, may also help providing more robust results.

Kinematic stability may then be evaluated by looking at the number ofinliers for the final spatial transformation model and comparing it tothe total number of matches or the total number of kept matches.

Kinematic stability may also be evaluated by using the final spatialtransformation and computing a similarity score on the region of overlapbetween the images after warping the target one onto the other one. Thesimilarity score may be one of the standard or less standard similarityscores used in medical imaging including, but not limited to, sum ofsquared differences, normalized correlation, mutual information,normalized gradient field and the like.

In this case, kinematic stability is evaluated by a registrationsimilarity score. It should be noted that a direct approach toregistration that optimizes the similarity score is also possible andmight in some cases lead to better results. In some other cases, even ifkinematic stability is evaluated in terms of similarity score, goingthrough the feature matching route may lead to more robust results thatare less prone to being trapped in local minima. Depending on the exactimplementation, computational costs might also largely vary depending onthe chosen route.

Although fitting a transformation model to the matching data can in somecases be really efficient, there might be cases where defining the modelis too complex to be usable in practice. According to another embodimentof the invention a more local approach to analyzing the matches betweentwo consecutive images for kinematic stability can be used.Advantageously, the invention allows to not focus on the exact model ofspatial transformation but to evaluate the probability to have a fairlyspatially consistent spatial transformation between images. For thispurposes, a similarity score that relies on the local translationsprovided by the descriptor matches is proposed.

According to one embodiment of the invention, a similarity score betweenconsecutive images can be created through a vote map. The vote map is a2D histogram that sums up the contribution of each local translationfound by the matched descriptors. Contribution can be weighted by afunction of the discrepancy between the two matched descriptors, by thequality of the association or can simply all have a unit weight.

The vote map uses discretized voting bins. Advantageously, in the caseof a regular grid for image description, the resolution of the vote mapcan be chosen to be equal to that of the description grid. In this case,the size of the vote map will typically be twice that of the grid toallow for all possible translations from one grid point in the firstimage to another grid point in the other image.

In the case of a perturbed grid or in the case of keypoint detection,choosing the resolution of the vote map can be done according to therequired accuracy.

It should be noted that the overlap between two images depends on theamplitude of the translation. Because of that, not all translations canreceive the same maximum number of vote. Actually, in a simple setup,only the identity transformation may receive all votes. If we consider atranslation of half the field of view in one dimension and if we userectangular images, the overlap will correspond to half an image meaningthat only half the matches can vote for the correct translation.

To account for this potential bias, the vote map can further be weightedaccording to the maximum number of potential voters per voting bin.Advantageously, the maximum number of potential voters for a giventranslation in the vote map may be computed thanks to a convolution oftwo mask images that represent the spatial organization of the gridsused for image description.

In some imaging devices, the field of view of the images is not squarebut may typically be of circular or any other form. To compute thenormalization of the vote map, a mask image where all valid descriptorpositions are filled with one and invalid ones with zero can be created.

After convolution of the masks, we obtain a contribution map containingthe ratio of potential contributors over the maximum number ofcontributors for each possible translation. The values are between 0and 1. According to one embodiment of the invention, we may want toconsider only the translations that can be voted by a sufficient numberof descriptor matches.

The vote map may be normalized as such. Each entry in the vote map geteither divided by the value of the contribution map if the value of thecontribution map is above a given threshold or get assigned to 0otherwise (i.e. if the value of the contribution map is below thethreshold).

Once the normalized vote map is computed, in the case where the spatialtransformation can be well represented by a translation, we willtypically observe a main peak in the vote map around the expectedtranslation.

In case of more complex spatial transformations, including non-linearones, many peaks will typically appear in the vote map, normalized ornot. According to one embodiment of out invention, all peaks are takeninto account to evaluate kinematic stability. For this, a simplethreshold on the values of the vote map can be done to select all votesthat are sufficiently consistent. All the values in the vote map thatcorrespond to selected consistent votes can then be summed up toevaluate an overall consistency related to kinematic stability.

The previous approach may ensure that only translations that are sharedon somewhat extended local image regions are taken into account.Although this may cover most of the important transformations we need,in some cases, a more refined approach might be required. According toanother embodiment of the invention, a match will be selected accordingto the following rule. Given a neighborhood of matches, a robustestimation of a simple transformation model is performed. The centermatch for this neighborhood may be selected depending on its distance tothe model transformation. This way only locally consistent matches arekept to evaluated overall consistency.

Advantageously, depending on the model of local spatial transformation,such selection may be performed by relying on a simple smoothing,filtering or regularization of the displacement field produced by thematches.

Once spatial transformation consistency between consecutive images iscomputed, a simple threshold on the consistency may be used as anindicator of kinematic stability.

To further reduce the computational complexity, a multi-scale approachmay be employed. As a first step, a coarse grid of descriptors may beused. While the lower granularity means the estimations derived fromthis grid are less accurate, the decrease in the number of descriptorsmakes the algorithm run much faster. Given the result found using thecoarse grid, we may already detect easy to match image pairs and easyimage pairs that cannot be matched. For the image pairs that are noteasy, we may run the algorithm using the fine grid. Advantageously, wemay decide to use fairly conservative rules to distinguish easy imagepairs.

Instead of using a coarse grid and then a fine grid for comparison, theinvention allows to achieve similar speed-up if the internal database isused to save the n-d trees built from the fine grids. If this is done, acoarse grid on one image can be matched efficiently to the fine grid ofthe other image. This is advantageous because using two coarse grids ofdescriptors, means that the discretization error is increased. Theremight thus be a chance that the grids are too severely mis-matched andthat the algorithm might not correctly indicate a pair of stableconsecutive images. However, by using one fine grid of descriptors, thediscretization error is kept similar to the complete fine grid case. Itis only the noise on the vote map that will be higher when using onecoarse grid.

If several description scales are used the same procedure may also beapplied in a standard multiscale fashion moving from the coarsest scaleto the finest one and stopping whenever a scale allows for making aconfident estimation of kinematic stability.

According to another embodiment, several scales may be used concurrentlyto create a multi-scale vote map on which the above analysis can beextended by working on multi-valued analysis.

Beyond stability of consecutive image, the notion of kinematic stabilitymay preferably also cover the idea that stable sub-sequences should notbe restricted to only one or a few isolated images and that stablesubsequences separated by only one or a few unstable images should bejoined.

For this purpose, as illustrated in FIG. 7A and FIG. 7B, according toone embodiment of the invention, mathematical morphological operationsin the temporal domain can be used. If analyses of consecutives imagesled to a timeline with a binary information (71, 72) of kinematicstability, a morphological closing operation (illustrated in FIG. 7A)may be used to fill small gaps (73) between stable subsequences while amorphological opening operation (illustrated in FIG. 7B) may be used toremove stable (75) but too short subsequences.

As illustrated in FIG. 7A and FIG. 7B, this approach may allow avoidingsome of the false negatives and false positives in our initial temporalsegmentation.

Instead of binarizing the result of the image-pairs kinematic stabilityanalysis before the mathematical morphology operations, the inventionalso allows for using signal processing tools directly on the continuousoutput of the kinematic analysis. Simple Gaussian smoothing, grayscalemathematical morphology, iterative methods, graph-cuts and the like maybe used for this purpose. For the graph-cut approach, one potentialembodiment would use the continuous kinematic stability analysis (or atransfer function of it) between two consecutive images as aregularization factor (smoothing term), and could use, as data term, aconstant factor, a pre-processing result or any other data-driven signalsuch as the standard deviation of the image or the matches and the like.

Use of an Advanced Internal Database

According to one embodiment, the invention may process the currentlyacquired image on the fly. An internal database that is initially emptymay be created and progressively enriched by the result of theon-the-fly processing of the previously acquired images. This internaldatabase may be updated upon at each update of the image buffer.

The internal database may store intermediate results that may becomputed at the region level, at the image level or at the videosubsequence level. Said intermediate results may include but are notlimited to:

-   -   global and local visual features;    -   global and local inter-frame similarity distances;    -   global and local displacement fields;    -   visual words built from visual feature clustering;    -   visual signatures;    -   similarity distances between video subsequences;    -   similarity distances from video subsequences of the video of        interest to videos of an external database;    -   a posteriori knowledge information extracted from an external        database containing already acquired and annotated images or        videos.

The internal database may for example include a graph-based structure,such as a k-d tree or a random forest, which supports the generation andthe treatment of the stored intermediate results. Said treatmentincludes but is not limited to:

-   -   clustering visual features;    -   computing a visual signature associated with a video        subsequence;    -   computing distances between visual signatures.        Classification-Based and Regression-Based Schemes

According to one embodiment, in the case of discrete outputs, the firstalgorithm of the invention is able to use a classifier to estimate thelabel corresponding to an image. The classifier might be a simplerule-based one or may rely on machine learning to be trained from anexternal training database where a set of images is associated withground truth data such as labels or annotations.

According to another embodiment, in the case of continuous outputs, thefirst algorithm of the invention is able to use a regression algorithmto estimate the label or continuous output corresponding to an image.The regression algorithm might be a simple least-squares regression oneor may rely on machine learning to be trained from an external trainingdatabase where a set of images is associated with continuous groundtruth data.

Machine learning tools potentially used by the first algorithm forclassification or regression purposes may be for example based onSupport Vector Machines, Adaptive Boosting, Content-Based ImageRetrieval followed by k-Nearest Neighbor voting, Artificial NeuralNetworks, Random Forests, and the like.

Visual Similarity Assessment and Clustering

According to one embodiment, the invention is able to operate in a fullyunsupervised manner by only relying on the image content of the video ofinterest.

The first algorithm may be a fully unsupervised clustering algorithmthat takes as input all the images stored in the buffer and provides asoutput a cluster associated with each image. According to oneembodiment, the cluster associated with an image can be mapped to acolor that can be superimposed on the timeline at the positioncorresponding to the image in the video buffer.

The unsupervised clustering algorithm may be based on K-Meansclustering, hierarchical clustering, Mean Shift clustering, Graph Cuts,Random Forest-based clustering, Random Ferns, or any other standardclustering algorithm. The clustering algorithm may use intermediateresults stored in the internal database.

According to one embodiment, a visual signature is built for each imagestored in the buffer using any adequate technique such as thebag-of-visual-words, Haralick features or invariant scatteringconvolution networks and relying on the internal database as a trainingdatabase. Then, the unsupervised clustering of the images may beperformed based on their visual signatures.

Coupling Interpretability Characterization and a Second Algorithm

If the first algorithm has provided at least one discrete output foreach image, a second algorithm may be applied to video subsequences madeof consecutive images of equal output, and provide at least one outputto be displayed. As mentioned earlier, such discrete output may bereferred to as a temporal segmentation of the video of interest. Thesecond algorithm may use at least one output of the first algorithm,intermediate results stored in the internal database, data stored in theexternal database and the like.

According to one embodiment, the first algorithm provides a means ofdetecting, in the video of interest, video subsequences that are optimalqueries for the second algorithm.

The second algorithm includes but is not limited to:

-   -   image or video mosaicing to create an image of larger field of        view from at least one video subsequence;    -   unsupervised clustering of video subsequences, for example to        cluster the video of interest into visual scenes;    -   unsupervised characterization of the video subsequences, for        example to estimate the visual atypicity of each video        subsequence;    -   supervised classification of the video subsequences, for example        to associate a predicted diagnostic or pathological class and a        prediction confidence level with a video subsequence or with the        complete video of interest    -   supervised regression of the video subsequences, for example to        estimate a probability of the entire video of interest of of        each video subsequence of belonging to a given pathological        class;    -   supervised characterization of the video subsequences, for        example to estimate the visual ambiguity of each video        subsequence or of the entire video of interest with respect to a        set of diagnostic or pathological classes;    -   content-based video or image retrieval, with at least one video        subsequence as query, for example to extract from an external        database already annotated videos that are visually similar to        the query.

According to one embodiment, when the second algorithm is acontent-based retrieval algorithm, the invention allows users, typicallyphysicians, to efficiently create, from the results of the firstalgorithm, reproducible queries for the second algorithm in asemi-automated fashion. This may, in some scenarios, allow boostingretrieval performance when compared to using uncut videos as queries orwhen compared to fully automated query construction. Such asemi-automated approach may also allow us to approach the performance ofcarefully constructed queries by a human expert.

To achieve this, our query construction approach may be decomposed intwo steps. In a first step, an automated temporal segmentation of theoriginal video into a set of subsequences of interest may be performedthanks to any of the previously described methods such as kinematicstability or image quality assessment. A second step consists in a fastuser selection of a subset of the segmented sub-sequences. The physicianmay simply be asked to keep or discard the subsequences provided by thefirst step. Although each of the possible sub-sequences may possiblycontain images of different tissue type, the segmentation step willtypically make each subsequence much more self-consistent than theoriginal uncut video. The simplified user interaction thus allows a fastand reproducible query construction and allows the physician toconstruct a query with sufficient visual similarity within and betweenthe selected subsequences.

In one variant, the user is asked to briefly review each segmentedsubsequence and click on the ones that are of interest to him. Becauseall this may happen during the procedure, the temporal segmentation mayadvantageously be compatible with real-time.

Given the user-chosen subset of subsequences, the invention may use thissubset to create a visual signature for the content-based retrievalalgorithm to query the external database. The most visually similar casemay then be presented to the physician along with any potentialannotations that may be attached to them.

In one variant, the bag of visual words method, Haralick features or anyother compatible method may be used to compute one signature per imagefor each image in a selected video subsequence. By averaging thesesignatures, each subsequence and each video can be associated with avisual signature that may be used for retrieval purposes.

In another variant, rather than computing one signature per video, eachsubsequence may be associated to one visual signature that may then beused for retrieval purposes. The retrieved cases for all subsequencesmay then be pooled and reused according to their visual similarity withtheir corresponding initial subsequence query.

As should be clear from the previous description, for computationalreasons, when both the first algorithm and the second algorithm rely onthe same intermediate computations, such computation may be performedonly once and shared across the two algorithms. This is for example thecase when relying on a common set of feature descriptors, such as aregular dense grid of SIFT, SURF and the like, for both temporalsegmentation based on kinematic stability and for content-basedretrieval based on bag of words.

While the foregoing written description of the invention enables one ofordinary skill to make and use what is considered presently to be thebest modes thereof, those of ordinary skill will understand andappreciate the existence of variations, combinations, and equivalents ofthe specific embodiments, methods, and examples herein. The inventionshould therefore not be limited by the above described embodiments,methods, and examples, but by all embodiments and methods within thescope and spirit of the invention as disclosed.

The invention claimed is:
 1. A method to support clinical decision bycharacterizing images acquired in sequence through a medical device,comprising: storing images in a buffer; for each image in the buffer,automatically determining, using a first algorithm, at least one outputbased on at least one image quantitative criterion, wherein the at leastone image quantitative criterion is one selected from the groupconsisting of kinematic stability, similarity between images,probability of belonging to a category, image or video typicity, imageor video atipicity, image quality, and presence of artifacts, andwherein the at least one output of the first algorithm comprises acluster associated with each image; and extracting, from the buffer, theimages according to said at least one output for each image.
 2. Themethod according to claim 1, wherein the at least one output of thefirst algorithm is a boolean, scalar or vector value.
 3. The methodaccording to claim 1, wherein a timeline is formed of temporal regionscorresponding to images with equal outputs.
 4. The method according toclaim 3, wherein said timeline is formed of temporal regionscorresponding to consecutive images with said equal outputs.
 5. Themethod according to claim 1, further comprising: selecting images withequal said at least one output of the first algorithm; and processingthe selected images using a second algorithm, the second algorithm beingconfigured to provide at least one second output.
 6. The methodaccording to claim 5, further comprising: displaying said at least onesecond output.
 7. The method according to claim 5, wherein the secondalgorithm is a content-based image or video retrieval algorithm.
 8. Themethod according to claim 5, wherein the second algorithm is based onimage or video classification.
 9. The method according to claim 5,wherein the second algorithm is an image or video mosaicing algorithm.10. The method according to claim 5, wherein the second algorithm usesan external database.
 11. The method according to claim 5, wherein thesecond algorithm is based on machine learning.
 12. The method accordingto claim 1, wherein the first algorithm uses an external database. 13.The method according to claim 1, wherein the first algorithm is based onmachine learning.
 14. A system to support clinical decision bycharacterizing images acquired in sequence through a medical device, thesystem comprising means for implementing the steps of a method accordingto claim 1.